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AC agent-health-optimizer

Automated agent diagnostics and self-optimization toolkit for OpenClaw. Run one command to score your agent's health (0-100, grade A+ to F), audit memory hygiene, detect cron job issues, and compare installed skills against ClawHub. Set up a weekly cron for continuous self-improvement with trend tracking. Use when you want to diagnose agent problems, optimize your setup, check for better skills, or automate periodic health checks.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 1 311 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Automated agent diagnostics and self-optimization toolkit for OpenClaw.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 43 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1311 tokens
    • 100Progress reporting. Reports progress

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -213 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 434: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (3 code blocks)
    • +3All 5 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.